Skip to main content

The Importance of Intents and Context in Chatbots

Humans are good in conversations, because they can understand the intent of any statement, also the context in which the statement is placed.

Today’s chatbots are equipped with intent recognition engine. Using machine learning, statements are mapped to intents, and the chabot can handle infinite variations of the same statement. Why is this technique useful? The following example cites this:

Consider you want to ask the contact details of a company. Now every person will ask it in a different way. Following are few of the variations that are possible:

  • Please share your contact details
  • Where do you reside
  • Where are you located
  • Can you share your location
  • Please provide your address
Now the intent of the above questions is “asking address”. So a answer can be mapped to the intent, rather than all the questions. This helps in managing the chatbot easy.

Before intent recognition came into existence, most popular being Wit.ai, recently acquired by Facebook, earlier chatbots were purely based on Artificial Intelligence Markup Language (AIML). AIML was purely a question-answering template. A lot of manual intervention was needed to make the chatbot sound intelligent. Based on pure pattern matching in the questions, it had a basic flaw - it was not able to handle the infinite variations in a question.

Context identification is still a difficult problem, and one of the core problems the AI community is working on. It is argued that, once this problem is solved, AI might have reached the Artificial General Intelligence level, which means the chatbot will be able to match up to human capabilities.

Intent recognition engines such as Wit.ai, Watson Conversation and Microsoft Bot Framework have changed the chatbot scene. Today it is very easy for anyone to develop a chatbot by training it on various intents. Context identification is handled by such engines using a manual process of creating stories or dialogues. A person who is training the chatbot has to write various dialogue flows to make the chatbot understand context.

We at Cere Labs, a Mumbai based startup in Artificial Intelligence, have developed successful chatbots that uses intent recognition. After training the chatbots on various intents, the chatbots have started sounding intelligent, and are able to answer most of the user’s questions. Look into this space as we elaborate on the process of intent recognition.

Let your users talk to your website by using chatbots. You can talk to our chatbot on Cere Lab’s facebook page - https://www.facebook.com/cerelabs/

Comments

  1. James Anthony20 July 2019 at 05:05

    We can build your chatbot from scratch, instilling the best practices of a conversational user experience. From setting up a chatbot on your Facebook Page, to implementing analytics so you can understand how users are becoming customers. Malaysia chatbots

    ReplyDelete
  2. Anonymous22 January 2022 at 21:09

    Play for Real Money with Our Bonus & Enjoy This
    There is 1xbet no カジノ シークレット shortage of free play offers for online casino gambling sites – the casino games you play for real money or for jeetwin free at

    ReplyDelete
  3. Ncrt Solutions16 April 2022 at 03:36

    I guess I am not the only one having all the enjoyment here keep up the good work. best ai chatbot

    ReplyDelete

Post a Comment

Popular posts from this blog

How is AI Saving the Future

Meanwhile the talk of AI being the number one risk of human extinction is going on, there are lot many ways it is helping humanity. Recent developments in Machine Learning are helping scientists to solve difficult problems ranging from climate change to finding the cure for cancer. It will be a daunting task for humans to understand enormous amount of data that is generated all over the world. Machine Learning is helping scientists to use algorithms that learn from data and find patterns. Below is a list of few of the problems AI is working on to help find solutions which otherwise would not have been possible: Cancer Diagnostics : Recently, scientists at University of California (UCLA) applied Deep Learning to extract features for achieving high accuracy in label-free cell classification. This technique will help in faster cancer diagnostics, and thus will save a lot of lives. Low Cost Renewable Energy : Artificial-intelligence is helping wind power forecasts of u...
6 comments
Read more

In the World of Document Similarity

How does a human infer whether two documents are similar? This question has dazzled cognitive scientists, and is one area under which a lot of research is taking place. As of  now there is no product that is able to match or surpass human capability in finding the similarity in documents. But things are improving in this domain, and companies such as IBM and Microsoft are investing a lot in this area. We at Cere Labs, an Artificial Intelligence startup based in Mumbai, also are working in this area, and have applied LDA and Word2Vec techniques, both giving us promising results: Latent Dirichlet Allocation (LDA) : LDA is a technique used mainly for topic modeling. You c an leverage on this topic modeling to find the similarity between documents. It is assumed that more the topics two documents overlap, more are the chances that those documents carry semantic similarity. You can study LDA in the following paper: https://www.cs.princeton.edu/~blei/papers/BleiNgJordan20...
Post a Comment
Read more

Understanding Generative Adversarial Networks - Part II

In "Understanding Generative Adversarial Networks - Part I" you gained a conceptual understanding of how GAN works. In this post let us get a mathematical understanding of GANs. The loss functions can be designed most easily using the idea of zero-sum games.  The sum of the costs of all players is 0.         This is the Minimax algorithm for GANs Let’s break it down. Some terminology: V(D, G) : The value function for a minimax game E(X) : Expectation of a random variable X, also equal to its average value D(x) : The discriminator output for an input x from real data, represents probability G(z): The generator's output when its given z from the noise distribution D(G(z)) : Combining the above, this represents the output of the discriminator when  given a generated image G(z) as input Now, as explained above, the discriminator is the maximizer and hence it tries to  maximize V(D, G) . The discriminator wa...
Post a Comment
Read more